Besides the above advanced recurrent architectures, there is a notion of adopting the standard 2D Convolutional Neural Networks (CNNs) for sequence data analysis. For instance, Bai et al. [1]} introduced a practical solution, named Temporal Convolutional Network that applies convolutional operations by sliding the
1D-kernels over a specific length of temporal data. Similar to the canonical RNN, the TCN is a causal system such that a response at time stamp \(t\)  is the result of 1D-kernels convolved only with input quantities from time stamp \(t\)  and before in the earlier time stamps. Thus, the TCN can be viewed as 1D fully convolutional networks (FCN) with causal convolutions. It is found that the TCN outperforms generic RNN, GRU, and LSTM models in certain applications, like polyphonic music modeling, character/word-level language modeling, and IoT anomaly detection [1]}, [3]}, [4]}.
